Projects per year
Personal profile
Biography
Ruben Loaiza-Maya is a Lecturer in the Department of Econometrics and Business Statistics. He completed his doctoral studies in econometrics at the University of Melbourne and his undergraduate degree in economics at the Universidad Nacional de Colombia (Medellin).
His research interests include:
Copula Modelling; Bayesian Estimation Methods; Time Series Analysis; Macroeconomic and Financial Forecasting;
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 1 Active
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Variational Inference for Intractable and Misspecified State Space Models
1/01/23 → 31/12/25
Project: Research
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ABC-based forecasting in misspecified state space models
Weerasinghe, C., Loaiza-Maya, R., Martin, G. M. & Frazier, D. T., 2024, (Accepted/In press) In: International Journal of Forecasting. 20 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access -
Bayesian forecasting in economics and finance: a modern review
Martin, G. M., Frazier, D. T., Maneesoonthorn, W., Loaiza-Maya, R., Huber, F., Koop, G., Maheu, J., Nibbering, D. & Panagiotelis, A., Apr 2024, In: International Journal of Forecasting. 40, 2, p. 811-839 29 p.Research output: Contribution to journal › Review Article › Research › peer-review
8 Citations (Scopus) -
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
Loaiza-Maya, R., Nibbering, D. & Zhu, D., Apr 2024, In: Journal of Econometrics. 241, 2, 18 p., 105741.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Loss-Based Variational Bayes Prediction
Frazier, D. T., Loaiza-Maya, R., Martin, G. M. & Koo, B., 2024, (Accepted/In press) In: Journal of Computational and Graphical Statistics. 31 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access1 Citation (Scopus) -
Natural gradient hybrid variational inference with application to deep mixed models
Zhang, W., Smith, M., Maneesoonthorn, W. & Loaiza-Maya, R., 2024, In: Statistics and Computing. 34, 6, 17 p., 185.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile